


import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
       if is_torch_available():
              import torch
       if is_tf_available():
              import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


_SCREAMING_SNAKE_CASE     :			Optional[int]        =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :			Dict        =							{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE     :			Dict        =							{
    """vocab_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """Salesforce/codegen-350M-mono""": (
            """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
        ),
    },
}

_SCREAMING_SNAKE_CASE     :			List[Any]        =							{
    """Salesforce/codegen-350M-mono""": 2_048,
}



class        A     (		snake_case_	):







     '''simple docstring'''







     lowerCamelCase :						Dict            =   VOCAB_FILES_NAMES
     lowerCamelCase :						str            =   PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase :						List[str]            =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase :						Union[str, Any]            =   ["""input_ids""", """attention_mask"""]
     lowerCamelCase :						Tuple            =   CodeGenTokenizer

     def __init__(				self    :							Tuple			,				_UpperCamelCase    :							str=None			,				_UpperCamelCase    :							Any=None			,				_UpperCamelCase    :							Any=None			,				_UpperCamelCase    :							str="<|endoftext|>"			,				_UpperCamelCase    :							int="<|endoftext|>"			,				_UpperCamelCase    :							str="<|endoftext|>"			,				_UpperCamelCase    :							Dict=False			,				**_UpperCamelCase    :							Optional[Any]			,				):
      super().__init__(
          _UpperCamelCase			,				_UpperCamelCase			,				tokenizer_file=_UpperCamelCase			,				unk_token=_UpperCamelCase			,				bos_token=_UpperCamelCase			,				eos_token=_UpperCamelCase			,				add_prefix_space=_UpperCamelCase			,				**_UpperCamelCase			,				)

      if kwargs.pop("add_bos_token"			,				_UpperCamelCase):
       _lowercase:       Dict						=				kwargs.pop("name_or_path"			,				"")
       raise ValueError(
           "Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
           "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
           f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
           f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
           "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
           " so that the fast tokenizer works correctly.")

      _lowercase:       str						=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
      if pre_tok_state.get("add_prefix_space"			,				_UpperCamelCase) != add_prefix_space:
       _lowercase:       str						=				getattr(_UpperCamelCase			,				pre_tok_state.pop("type"))
       _lowercase:       int						=				add_prefix_space
       _lowercase:       Dict						=				pre_tok_class(**_UpperCamelCase)

      _lowercase:       Tuple						=				add_prefix_space

     def 					UpperCAmelCase__       (				self    :							str			,				*_UpperCamelCase    :							Optional[Any]			,				**_UpperCamelCase    :							str):
      _lowercase:       Optional[Any]						=				kwargs.get("is_split_into_words"			,				_UpperCamelCase)
      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*_UpperCamelCase			,				**_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							Any			,				*_UpperCamelCase    :							Optional[int]			,				**_UpperCamelCase    :							Any):
      _lowercase:       Tuple						=				kwargs.get("is_split_into_words"			,				_UpperCamelCase)

      assert self.add_prefix_space or not is_split_into_words, (
          f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*_UpperCamelCase			,				**_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							str			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							List[Any] = None):
      _lowercase:       List[Any]						=				self._tokenizer.model.save(_UpperCamelCase			,				name=_UpperCamelCase)
      return tuple(_UpperCamelCase)

     def 					UpperCAmelCase__       (				self    :							List[Any]			,				_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							Any = False			,				_UpperCamelCase    :							Optional[int] = None			,				_UpperCamelCase    :							Optional[int] = None			,				**_UpperCamelCase    :							Union[str, Any]			,				):
      _lowercase:       List[Any]						=				super().decode(
          token_ids=_UpperCamelCase			,				skip_special_tokens=_UpperCamelCase			,				clean_up_tokenization_spaces=_UpperCamelCase			,				**_UpperCamelCase			,				)

      if truncate_before_pattern is not None and len(_UpperCamelCase) > 0:
       _lowercase:       Any						=				self.truncate(_UpperCamelCase			,				_UpperCamelCase)

      return decoded_text






     def 					UpperCAmelCase__       (				self    :							Dict			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Tuple):
      def find_re(_UpperCamelCase    :							Tuple			,				_UpperCamelCase    :							int			,				_UpperCamelCase    :							Dict):
       _lowercase:       Any						=				pattern.search(_UpperCamelCase			,				_UpperCamelCase)
       return m.start() if m else -1

      _lowercase:       Optional[Any]						=				[re.compile(_UpperCamelCase			,				re.MULTILINE) for pattern in truncate_before_pattern]

      _lowercase:       str						=				list(re.finditer("^print"			,				_UpperCamelCase			,				re.MULTILINE))

      if len(_UpperCamelCase) > 1:
       _lowercase:       Union[str, Any]						=				completion[: prints[1].start()]

      _lowercase:       Dict						=				list(re.finditer("^def"			,				_UpperCamelCase			,				re.MULTILINE))

      if len(_UpperCamelCase) > 1:
       _lowercase:       str						=				completion[: defs[1].start()]

      _lowercase:       str						=				0

      _lowercase:       List[str]						=				[
          pos for pos in [find_re(_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase) for terminal in terminals] if pos != -1
      ]

      if len(_UpperCamelCase) > 0:
       return completion[: min(_UpperCamelCase)]
      else:
       return completion


"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE__:int									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use DeformableDetrImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
